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Last active Jan 21, 2018
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Image Captioning LSTM

##Information

name: LSTM image captioning model based on CVPR 2015 paper "Show and tell: A neural image caption generator" and code from Karpathy's NeuralTalk.

model_file: https://s3-us-west-1.amazonaws.com/nervana-modelzoo/image_caption_flickr8k.py

model_weights: https://s3-us-west-1.amazonaws.com/nervana-modelzoo/image_caption_flickr8k.p

neon_version: v1.0.rc1

neon_commit: 2169b093fbba0c189021a941d286c7a98c0c6c6c

gist_id: 7e76e90664f935c6f65d

##Description The LSTM model is trained on the flickr8k dataset using precomputed VGG features from http://cs.stanford.edu/people/karpathy/deepimagesent/. Model details can be found in the following CVPR-2015 paper:

Show and tell: A neural image caption generator.
O. Vinyals, A. Toshev, S. Bengio, and D. Erhan.  
CVPR, 2015 (arXiv ref. cs1411.4555)

The model was trained for 15 epochs where 1 epoch is 1 pass over all 5 captions of each image. Training data was shuffled each epoch. To evaluate on the test set, download the model and weights, and run:

    python image_caption.py --model_file [path_to_weights]

##Performance For testing, the model is only given the image and must predict the next word until a stop token is predicted. Greedy search is currently used by just taking the max probable word each time. Using the bleu score evaluation script from https://raw.githubusercontent.com/karpathy/neuraltalk/master/eval/ and evaluating against 5 reference sentences the results are below.

BLEU Score
B-1 54.2
B-2 32.6
B-3 19.3
B-4 12.3

A few things that were not implemented are beam search, l2 regularization, and ensembles. With these things, performance would be a bit better.

@nickzuck

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@nickzuck nickzuck commented Jan 20, 2017

Access Denied on the model file

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